学习Python3 Dlib19.7进行人脸面部识别

(编辑：jimmy 日期: 2025/10/27 浏览：3 次 )

0.引言

自己在下载dlib官网给的example代码时，一开始不知道怎么使用，在一番摸索之后弄明白怎么使用了；

现分享下 face_detector.py 和 face_landmark_detection.py 这两个py的使用方法；

1.简介

python:　　3.6.3

dlib:　　　 19.7

利用dlib的特征提取器，进行人脸矩形框的特征提取：　　

dets = dlib.get_frontal_face_detector(img)

利用dlib的68点特征预测器，进行人脸 68点特征提取：

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
shape = predictor(img, dets[0])

效果：

(a) face_detector.py

b) face_landmark_detection.py

2.py文件功能介绍

face_detector.py :

识别出图片文件中一张或多张人脸，并用矩形框框出标识出人脸；

link: http://dlib.net/cnn_face_detector.py.html

face_landmark_detection.py ：在face_detector.py的识别人脸基础上，识别出人脸部的具体特征部位：下巴轮廓、眉毛、眼睛、嘴巴，同样用标记标识出面部特征；

link: http://dlib.net/face_landmark_detection.py.html

2.1. face_detector.py

官网给的face_detector.py

#!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
#  This example program shows how to find frontal human faces in an image. In
#  particular, it shows how you can take a list of images from the command
#  line and display each on the screen with red boxes overlaid on each human
#  face.
#
#  The examples/faces folder contains some jpg images of people. You can run
#  this program on them and see the detections by executing the
#  following command:
#    ./face_detector.py ../examples/faces/*.jpg
#
#  This face detector is made using the now classic Histogram of Oriented
#  Gradients (HOG) feature combined with a linear classifier, an image
#  pyramid, and sliding window detection scheme. This type of object detector
#  is fairly general and capable of detecting many types of semi-rigid objects
#  in addition to human faces. Therefore, if you are interested in making
#  your own object detectors then read the train_object_detector.py example
#  program. 
#
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
#  You can install dlib using the command:
#    pip install dlib
#
#  Alternatively, if you want to compile dlib yourself then go into the dlib
#  root folder and run:
#    python setup.py install
#  or
#    python setup.py install --yes USE_AVX_INSTRUCTIONS
#  if you have a CPU that supports AVX instructions, since this makes some
#  things run faster. 
#
#  Compiling dlib should work on any operating system so long as you have
#  CMake and boost-python installed. On Ubuntu, this can be done easily by
#  running the command:
#    sudo apt-get install libboost-python-dev cmake
#
#  Also note that this example requires scikit-image which can be installed
#  via the command:
#    pip install scikit-image
#  Or downloaded from http://scikit-image.org/download.html. 
import sys
import dlib
from skimage import io
detector = dlib.get_frontal_face_detector()
win = dlib.image_window()
for f in sys.argv[1:]:
  print("Processing file: {}".format(f))
  img = io.imread(f)
  # The 1 in the second argument indicates that we should upsample the image
  # 1 time. This will make everything bigger and allow us to detect more
  # faces.
  dets = detector(img, 1)
  print("Number of faces detected: {}".format(len(dets)))
  for i, d in enumerate(dets):
    print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
      i, d.left(), d.top(), d.right(), d.bottom()))
  win.clear_overlay()
  win.set_image(img)
  win.add_overlay(dets)
  dlib.hit_enter_to_continue()
# Finally, if you really want to you can ask the detector to tell you the score
# for each detection. The score is bigger for more confident detections.
# The third argument to run is an optional adjustment to the detection threshold,
# where a negative value will return more detections and a positive value fewer.
# Also, the idx tells you which of the face sub-detectors matched. This can be
# used to broadly identify faces in different orientations.
if (len(sys.argv[1:]) > 0):
  img = io.imread(sys.argv[1])
  dets, scores, idx = detector.run(img, 1, -1)
  for i, d in enumerate(dets):
    print("Detection {}, score: {}, face_type:{}".format(
      d, scores[i], idx[i]))

为了方便理解，修改增加注释之后的 face_detector.py

import dlib
from skimage import io
# 使用特征提取器frontal_face_detector
detector = dlib.get_frontal_face_detector()
# path是图片所在路径
path = "F:/code/python/P_dlib_face/pic/"
img = io.imread(path+"1.jpg")
# 特征提取器的实例化
dets = detector(img)
print("人脸数：", len(dets))
# 输出人脸矩形的四个坐标点
for i, d in enumerate(dets):
  print("第", i, "个人脸d的坐标：",
     "left:", d.left(),
     "right:", d.right(),
     "top:", d.top(),
     "bottom:", d.bottom())
# 绘制图片
win = dlib.image_window()
# 清除覆盖
#win.clear_overlay()
win.set_image(img)
# 将生成的矩阵覆盖上
win.add_overlay(dets)
# 保持图像
dlib.hit_enter_to_continue()

对test.jpg进行人脸检测：　

结果：

图片窗口结果：

输出结果：　　　

人脸数： 1
第 0 个人脸： left: 79 right: 154 top: 47 bottom: 121
Hit enter to continue

#!/usr/bin/python # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt # # This example program shows how to find frontal human faces in an image and # estimate their pose. The pose takes the form of 68 landmarks. These are # points on the face such as the corners of the mouth, along the eyebrows, on # the eyes, and so forth. # # The face detector we use is made using the classic Histogram of Oriented # Gradients (HOG) feature combined with a linear classifier, an image pyramid, # and sliding window detection scheme. The pose estimator was created by # using dlib's implementation of the paper: # One Millisecond Face Alignment with an Ensemble of Regression Trees by # Vahid Kazemi and Josephine Sullivan, CVPR 2014 # and was trained on the iBUG 300-W face landmark dataset (see # https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/): # C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. # 300 faces In-the-wild challenge: Database and results. # Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016. # You can get the trained model file from: # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2. # Note that the license for the iBUG 300-W dataset excludes commercial use. # So you should contact Imperial College London to find out if it's OK for # you to use this model file in a commercial product. # # # Also, note that you can train your own models using dlib's machine learning # tools. See train_shape_predictor.py to see an example. # # # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE # You can install dlib using the command: # pip install dlib # # Alternatively, if you want to compile dlib yourself then go into the dlib # root folder and run: # python setup.py install # or # python setup.py install --yes USE_AVX_INSTRUCTIONS # if you have a CPU that supports AVX instructions, since this makes some # things run faster. # # Compiling dlib should work on any operating system so long as you have # CMake and boost-python installed. On Ubuntu, this can be done easily by # running the command: # sudo apt-get install libboost-python-dev cmake # # Also note that this example requires scikit-image which can be installed # via the command: # pip install scikit-image # Or downloaded from http://scikit-image.org/download.html. import sys import os import dlib import glob from skimage import io if len(sys.argv) != 3: print( "Give the path to the trained shape predictor model as the first " "argument and then the directory containing the facial images.\n" "For example, if you are in the python_examples folder then " "execute this program by running:\n" " ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n" "You can download a trained facial shape predictor from:\n" " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2") exit() predictor_path = sys.argv[1] faces_folder_path = sys.argv[2] detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) win = dlib.image_window() for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")): print("Processing file: {}".format(f)) img = io.imread(f) win.clear_overlay() win.set_image(img) # Ask the detector to find the bounding boxes of each face. The 1 in the # second argument indicates that we should upsample the image 1 time. This # will make everything bigger and allow us to detect more faces. dets = detector(img, 1) print("Number of faces detected: {}".format(len(dets))) for k, d in enumerate(dets): print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( k, d.left(), d.top(), d.right(), d.bottom())) # Get the landmarks/parts for the face in box d. shape = predictor(img, d) print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1))) # Draw the face landmarks on the screen. win.add_overlay(shape) win.add_overlay(dets) dlib.hit_enter_to_continue()

import dlib from skimage import io # 使用特征提取器frontal_face_detector detector = dlib.get_frontal_face_detector() # dlib的68点模型 path_pre = "F:/code/python/P_dlib_face/" predictor = dlib.shape_predictor(path_pre+"shape_predictor_68_face_landmarks.dat") # 图片所在路径 path_pic = "F:/code/python/P_dlib_face/pic/" img = io.imread(path_pic+"1.jpg") # 生成dlib的图像窗口 win = dlib.image_window() win.clear_overlay() win.set_image(img) # 特征提取器的实例化 dets = detector(img, 1) print("人脸数：", len(dets)) for k, d in enumerate(dets): print("第", k, "个人脸d的坐标：", "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom()) # 利用预测器预测 shape = predictor(img, d) # 绘制面部轮廓 win.add_overlay(shape) # 绘制矩阵轮廓 win.add_overlay(dets) # 保持图像 dlib.hit_enter_to_continue()